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1st International Conference on Mathematical Modelling, Computational Intelligence and Renewable energy, MMCITRE 2020

作者： Singh, Sudhanshu V. Elgarhy, Mohammed Ahmad, Zubair Sharma, Vikas Kumar Hamedani, Gholamhossein G. Ahmedabad Gujarat India Vice Presidency for Graduate Studies and Scientific Research University of Jeddah Jeddah23218 Saudi Arabia Department of Statistics Quaid-I-Azam University Islamabad45320 Pakistan Department of Statistics Institute of Science Banaras Hindu University Varanasi India Department of Mathematics Statistics and Computer Science Marquette University MilwaukeeWI53201-1881 United States

ISBN: (纸本)9789811599521

This article deals with the Teissier/Muth distributions. Some properties as well as four members of Teissier-G family are provided. Definite formulae for ordinary moments, mean deviation, quantile function and order statistics are given. Some characterizations owing to the truncated moments, hazard function and conditional expectations are derived. We consider the parametric estimation of the family along with one of its sub-model. The use of the Teissier–Weibull family is revealed depending on two real data sets. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Maximum likelihood estimation

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Transversity generalized parton distributions in spin-3/2 particles

arXiv

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arXiv 2024年

作者： Fu, Dongyan Dong, Yubing Kumano, S. Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China School of Physical Sciences University of Chinese Academy of Sciences Beijing101408 China Department of Mathematics Physics and Computer Science Faculty of Science Japan Women’s University Mejirodai 2-8-1 Tokyo112-8681 Japan Theory Center Institute of Particle and Nuclear Studies KEK Oho 1-1Ibaraki Tsukuba305-0801 Japan Institute of Modern Physics Chinese Academy of Sciences Lanzhou730000 China

The definitions of the quark and gluon transversity generalized parton distributions (GPDs) in spin-3/2 particles are obtained in the light-cone gauge. It is found that they contain 16 independent components for each parton. Their even or odd property is found in terms of skewness variable, and the odd transversity GPDs vanish in the forward limit. There are 16 amplitudes with helicity flip of quark or gluon. We conclude that all the amplitudes, unpolarized, polarized, and transversity amplitudes, have a common factor F(ζ) carrying all the complex part. Here, the variable ζ is the helicity difference ζ = (λ′ − λ) − (µ′ − µ) for the amplitude Aλ′µ′,λµ. This kinematical factor is associated with the transfer of the orbital angular momentum in the quark- and gluon-spin-3/2 particle scattering amplitudes. We also derive another main physical quantity, transversity distribution, from the transversity GPDs in the forward limit for the spin-3/2 particles. Copyright © 2024, The Authors. All rights reserved.

关键词： Elementary particles

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Over-the-air Wireless Federated Learning Model for Generative AI

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IEEE Network 2025年

作者： Zheng, Jie Niyato, Dusit Zhang, Haijun Du, Hongyang Wang, Jiacheng Kang, Jiawen Xiong, Zehui Northwest University State-Province Joint Engineering and Research Center of Advanced Networking and Intelligent Information Services School of Information Science and Technology Shaanxi 710127 China Nanyang Technological University School of Computer Science and Engineering Singapore639798 Singapore University of Science and Technology Beijing Institute of Artificial Intelligence Beijing100083 China University of Hong Kong Department of electrical and electronic engineering Hong Kong Hong Kong Guangdong University of Technology Automation of School Guangzhou510006 China Singapore University of Technology and Design Pillar of Information Systems Technology and Design Singapore

Generative artificial intelligence (GenAI) technologies represent an important advancement in the field of AI, particularly for their capabilities in text and image generation. Over-the-air wireless federated learning (OA-WFL) can utilize the wireless waveform superposition property to achieve efficient model aggregation, thereby providing support for GenAI training and deployment. Therefore, this article investigates the support of OA-WFL for GenAI, focusing on potential applications and specific examples. We first discuss the OA-WFL and GenAI models, emphasizing their functionalities and the potential benefits arising from their interaction. We then explore its application in various GenAI scenarios, including large-scale edge device content generation, efficient distributed training of GenAI models, and reduction of bandwidth requirements and device load. Next, a framework is proposed to apply OA-WFL to diffusion models, such as those used in image generation, and validate its effectiveness through simulation results. Finally, we discuss prospective research directions for the application of OA-WFL for GenAI. © 2025 IEEE.

关键词： Federated learning

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知识引导下生成式模型协同合作与联合进化——后大模型时代的发展蓝图（英文）

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Engineering 2025年第1期 87-100页

作者：吴飞沈弢 Thomas B?ck 陈静远黄刚金耀初况琨李孟择卢策吾苗嘉旭王永威魏颖吴帆严骏驰杨红霞杨易张圣宇赵洲庄越挺潘云鹤 College of Computer Science and Technology Zhejiang University Leiden Institute of Advanced Computer Science Leiden University College of Electrical Engineering Zhejiang University School of Engineering Westlake University Department of Computer Science and Engineering Hong Kong University of Science and Technology Department of Computer Science and Engineering Shanghai Jiao Tong University School of Cyber Science and Technology Sun Yat-Sen University Department of Computing The Hong Kong Polytechnic University

Large language models(LLMs) have significantly advanced artificial intelligence(AI) by excelling in tasks such as understanding, generation, and reasoning across multiple modalities. Despite these achievements, LLMs have inherent limitations including outdated information, hallucinations, inefficiency, lack of interpretability, and challenges in domain-specific accuracy. To address these issues, this survey explores three promising directions in the post-LLM era: knowledge empowerment, model collaboration, and model co-evolution. First, we examine methods of integrating external knowledge into LLMs to enhance factual accuracy, reasoning capabilities, and interpretability, including incorporating knowledge into training objectives, instruction tuning, retrieval-augmented inference, and knowledge prompting. Second, we discuss model collaboration strategies that leverage the complementary strengths of LLMs and smaller models to improve efficiency and domain-specific performance through techniques such as model merging, functional model collaboration, and knowledge injection. Third, we delve into model co-evolution, in which multiple models collaboratively evolve by sharing knowledge, parameters, and learning strategies to adapt to dynamic environments and tasks, thereby enhancing their adaptability and continual learning. We illustrate how the integration of these techniques advances AI capabilities in science, engineering, and society—particularly in hypothesis development, problem formulation, problem-solving, and interpretability across various domains. We conclude by outlining future pathways for further advancement and applications.

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Design and Implementation of Blockchain Based Technology for Supply Chain Quality Management: Challenges and Opportunities

Design and Implementation of Blockchain Based Technology for...

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International Conference on Computation, Automation and Knowledge Management (ICCAKM)

作者： Ashutosh Singh Somanchi Hari Krishna Anupama Tadamarla Sonali Gupta Atish Mane Mahabub Basha Institute of Business Management GLA University Mathura Uttar Pradesh Department of Business Management Vignana Bharathi Institute of Technology Aushapur Village Ghatkesar Mandal Telangana ICFAI Business School Pune Department of Computer Science and Engineering Graphic Era Hill University Dehradun Uttarakhand Mechanical Engineering Bharati Vidyapeeth's college of Engineering Lavale Pune Maharashtra India Department of Commerce International Institute of Business Studies Bengaluru

The research study explores the Supply Chain Quality Management (SCQM), the ways for ensuring supply consistency at reasonable rates while maintaining high quality requirements in the product network. To achieve the objectives of modern manufacturing, it is necessary to handle the enormous information generated by the many production planning participants. In a supply chain, however, it might be challenging to meet with vendor faith and confidentiality standards because of the nature of the business. While this article is concerned with a possible use of blockchain in SCQM, it also provides an in-depth technological study of its technological viability, as well as strategies for migrating from the present methodologies to blockchain-based platforms. It is also mentioned how blockchain may be used in an interesting place to keep recommendation data. Blockchain combined with IoT may enhance SCQM in an advanced Manufacturing framework as well compliances of obstacles occurs with lots of prospects.

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A Lightweight Approach to Real-Time Speaker Diarization: From Audio Toward Audio-Visual Data Streams

SSRN

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SSRN 2023年

作者： Kynych, Frantisek Cerva, Petr Zdansky, Jindrich Svendsen, Torbjørn Salvi, Giampiero Faculty of Mechatronics Informatics and Interdisciplinary Studies Technical University of Liberec Studentska 2 Liberec461 17 Czech Republic Department of Electronic Systems Norwegian University of Science and Technology Torgarden TrondheimNO-7491 Norway KTH Royal Institute of Technology School of Electrical Engineering and Computer Science Brinellvägen 8 StockholmSE-100 44 Sweden

This manuscript deals with the task of real-time speaker diarization (SD) for stream-wise data processing. Therefore, in contrast to most of the existing papers, it considers not only the accuracy but also the computational demands of individual investigated methods. We first propose a new lightweight scheme allowing us to perform speaker diarization of streamed audio data. Our approach utilizes a modified residual network with squeeze-and-excitation blocks (SE-ResNet-34) to extract speaker embeddings in an optimized way using cached buffers. These embeddings are subsequently used for voice activity detection (VAD) and block-online k-means clustering with a look-ahead *** described scheme yields results similar to the reference offline system while operating solely on a CPU with a low real-time factor (RTF) below 0.1 and a constant latency of around 5.5 seconds. In the next part of the work, our research moves toward much more demanding and complex real-time processing of audio-visual data *** this purpose, we extend the above-mentioned scheme for audio data processing by adding an audio-video module. This module utilizes SyncNet combined with visual embeddings for identity tracking. Our resulting multi-modal SD framework then combines the outputs from audio and audio-video modules by using a new overlap-based fusion strategy. It yields diarization error rates that are competitive with the existing state-of-the-art offline audio-visual methods while allowing us to process various audio-video streams, e.g., from internet or TV broadcasts, in real-time and with the same latency as for audio data processing. © 2023, The Authors. All rights reserved.

关键词： Embeddings

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Cloud Computing Applications For Protecting the Information of Healthcare department Using Smart Internet of Things Appliance

Cloud Computing Applications For Protecting the Information ...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Amit Kumar Jain Dhyana Sharon Ross M. Kishore Babu Dharamvir Dipesh Uike Durgaprasad Gangodkar Department of Computer Science Delhi University Delhi Loyola Institute of Business Administration (LIBA) Chennai Tamil Nadu India KL Business School Koneru Lakshmaih Education Foundation Guntur Andhra Pradesh India Department of MCA The Oxford College of Engineering Bangalore India Dr. Ambedkar Institute of Management Studies and Research Nagpur Maharashtra India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India

Combining cloud computing and Internet of Things concept is the emerging trend for online management efficiently and the sensor data processing. Pervasive healthcare sector or using the Internet of Things paradigm and the study offers the platform the disc based on cloud computing for the administration of information healthcare department. The development of clear and organized healthcare monitoring systems is made possible by the Cloud IoT convergence of the Cloud and IoT. Different types of healthcare applications use various IoT based and cloud-based application for transmission of data and to connect with each other that enables the provision of clinical healthcare solutions effectively. The research study proposes a novel hybrid system that uses IoT smart appliance using metaheuristic swarm intelligence technique for storing data into the cloud then to balance the load with real-time providers at low-cost that receives data from the sensor devices of hospital and stored in cloud. The computer resources are revolutionizing algorithms for individualized inference and actions in health management and the data is protected using blockchain technology.

关键词： Cloud computing Medical devices Hospitals Metaheuristics Real-time systems Blockchains Internet of Things

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The effect of emojis on arabic text in sentiment analysis using deep learning

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AIP Conference Proceedings 2025年第1期3255卷

作者： Suhad Nasrallah Taraf Ahmed T. Sadiq Informatics Institute for Postgraduate Studies Iraqi Commission for Computers & Informatics Baghdad Iraq Department of computer science College of science University of Technology-Iraq Baghdad Iraq

The emergence of social media has enabled the swift proliferation of explanatory content across various domains, encompassing advertisements, movies, politics, and economics. The growth has been ascertained through the escalation in emotional analysis, encompassing diverse facets. The Arabic review and OMCD big data sets were amalgamated with Twitter data. The textual content was segregated from emojis, and various word embedding techniques were applied, including Spacy (W2V), Fast Text, and Arabic Bidirectional Encoding Representation (AraBERT), resulting in record-high percentages. Convolutional neural networks (CNN), long short-term memory (LSTM), and recurrent neural networks (RNN) were employed in the development of sentiment analysis models. The assessment of model performance was predicated on achieving optimal accuracy, utilizing the voting method to formulate decisions based on weighting. Deep learning (DL) methodologies with the AST (Arabic et al.) dataset resulted in accurate textual content and emoji models. The symbols occupied exact proportions and significantly impacted the final classification. In contrast, for OMCD (Moroccan Offensive Comments Dataset), the text had a more significant impact due to its small number of symbols.

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Non-Sinusoidal Temporal Modulation in Circuits for Non-Reciprocity and Power Transfer

Non-Sinusoidal Temporal Modulation in Circuits for Non-Recip...

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European Conference on Antennas and Propagation, EuCAP

作者： Juan Rafael Sánchez-Martinez Mario Pérez-Escribano Sergio Ortiz-Ruiz Juan F. Valenzuela-Valdés Pablo Padilla Carlos Molero Department of Signal Theory Telematics and Communications Research Centre for Information and Communication Technologies (CITIC-UGR) University of Granada Granada Spain Telecommunication Research Institute (TELMA) Universidad de Málaga E.T.S. Ingenieria de Telecomunicación Malaga Spain Department of Electronics and Computer Science Pervasive Electronics Advanced Research Laboratory Centre for Information and Communication Technologies (CITIC-UGR) University of Granada Granada Spain

ISBN: (数字)9788831299107

ISBN: (纸本)9798350366327

In this paper, we demonstrate the improvement in circuit performance when time-varying circuits are modulated by non-sinusoidal waveforms. Specially we focus on triangular and rectangular modulations. By this, bandpass filters are designed and compared to conventional cases in order to exhibit the enhancement. Specially, the bandpass filters are desined to invoke non-reciprocity, an exotic and very interesting feature in time-modulated systems. A method of analysis based on the spectral domain is employed, establishing a general approach that is valid and applicable to other types of signals, as long as it is possible to obtain its decomposition in Fourier series. The results obtained from our model match those simulated using the Keysight ADS, thus validating the present approach.

关键词： Band-pass filters Analytical models Time varying circuits Resonator filters Modulation Spatiotemporal phenomena Fourier series Resonators Integrated circuit modeling Spectral analysis

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Tumor Detection, Segmentation and Classification Challenge on Automated 3D Breast Ultrasound: The TDSC-ABUS Challenge

arXiv

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arXiv 2025年

作者： Luo, Gongning Xu, Mingwang Chen, Hongyu Liang, Xinjie Tao, Xing Ni, Dong Jeong, Hyunsu Kim, Chulhong Stock, Raphael Baumgartner, Michael Kirchhoff, Yannick Rokuss, Maximilian Maier-Hein, Klaus Yang, Zhikai Fan, Tianyu Boutry, Nicolas Tereshchenko, Dmitry Moine, Arthur Charmetant, Maximilien Sauer, Jan Du, Hao Bai, Xiang-Hui Raikar, Vipul Pai Montoya-Del-Angel, Ricardo Martí, Robert Luna, Miguel Lee, Dongmin Qayyum, Abdul Mazher, Moona Guo, Qihui Wang, Changyan Awasthi, Navchetan Zhao, Qiaochu Wang, Wei Wang, Kuanquan Wang, Qiucheng Dong, Suyu School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China Department of Mathematics Faculty of Science National University of Singapore Singapore National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Medical School Shenzhen University Shenzhen China Laboratory Shenzhen University Shenzhen China School of Biomedical Engineering and Informatics Nanjing Medical University Nanjing China Pohang Korea Republic of Heidelberg Division of Medical Image Computing Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Germany Heidelberg Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Karlsruhe Heidelberg Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Germany Department of Biomedical Engineering and Health KTH Royal Institute of Technology Stockholm Sweden France FathomX Singapore Saw Swee Hock School of Public Health National University of Singapore Singapore Philips Research University of Girona Spain Department of Robotics and Mechatronics Engineering DGIST Korea Republic of Department of Interdisciplinary Studies of Artificial Intelligence DGIST Korea Republic of National Heart and Lung Institute Faculty of Medicine Imperial College London London United Kingdom Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom Lab School of Communication and Information Engineering Shanghai University Shanghai China Faculty of Science Mathematics and Computer Science Informatics Institute University of Amsterdam Amsterdam1090 GH Netherlands Department of Biomedical Engineering and Physics Amsterdam UMC Amsterdam1081 HV Netherlands Xi’an Jiaotong-Liverpool University China Department of Ultrasound Harbin Medical University Cancer Hospital No. 150 Haping Road Nangang

Breast cancer is one of the most common causes of death among women worldwide. Early detection helps in reducing the number of deaths. Automated 3D Breast Ultrasound (ABUS) is a newer approach for breast screening, which has many advantages over handheld mammography such as safety, speed, and higher detection rate of breast cancer. Tumor detection, segmentation, and classification are key components in the analysis of medical images, especially challenging in the context of 3D ABUS due to the significant variability in tumor size and shape, unclear tumor boundaries, and a low signal-to-noise ratio. The lack of publicly accessible, well-labeled ABUS datasets further hinders the advancement of systems for breast tumor analysis. Addressing this gap, we have organized the inaugural Tumor Detection, Segmentation, and Classification Challenge on Automated 3D Breast Ultrasound 2023 (TDSC-ABUS2023). This initiative aims to spearhead research in this field and create a definitive benchmark for tasks associated with 3D ABUS image analysis. In this paper, we summarize the top-performing algorithms from the challenge and provide critical analysis for ABUS image examination. We offer the TDSC-ABUS challenge as an open-access platform at https://***/ to benchmark and inspire future developments in1algorithmic research. © 2025, CC BY.

关键词： Image segmentation

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